US8719452B1 - Correction of client-assigned timestamps - Google Patents
Correction of client-assigned timestamps Download PDFInfo
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- US8719452B1 US8719452B1 US13/194,892 US201113194892A US8719452B1 US 8719452 B1 US8719452 B1 US 8719452B1 US 201113194892 A US201113194892 A US 201113194892A US 8719452 B1 US8719452 B1 US 8719452B1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3466—Performance evaluation by tracing or monitoring
- G06F11/3476—Data logging
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/434—Disassembling of a multiplex stream, e.g. demultiplexing audio and video streams, extraction of additional data from a video stream; Remultiplexing of multiplex streams; Extraction or processing of SI; Disassembling of packetised elementary stream
- H04N21/4344—Remultiplexing of multiplex streams, e.g. by modifying time stamps or remapping the packet identifiers
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
- G06F2201/835—Timestamp
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
- G06F2201/86—Event-based monitoring
Definitions
- the present specification relates to client-server communications.
- a client-server model of computing defines some computer resources as a server, and other computer resources as clients. Computing tasks are partitions, with some tasks carried out by the client, and some tasks carried out by the server.
- a single server may provide services to many clients, and a single client may access many resources by communicating with many servers.
- a single computer device may host both a server and one or more attached clients.
- each client and each server may be associated with one, or a group of, separate computers.
- a server may receive messages from one or more clients that are not synchronized with the server's clocks. These messages may be associated with time data that is accurate according to their respective client's clocks, but inaccurate according to the server's clock.
- the server may compare client-supplied transmission time data with server-supplied reception time data. The difference between the transmission time data and the reception data, after accounting for latencies, may be used to identify the difference between a server's and client's clocks. This difference may be used to modify time data from the client so that that time data is accurate from the server's perspective.
- data that (i) has been assigned timestamps by a client and (ii) has been communicated from the client device to a server may be assigned corrected timestamps by the server, and may be stored at the server according to the corrected timestamps.
- Correcting a client-assigned timestamp may include comparing a client-assigned transmission time associated with the data to a server-assigned receipt time associated with the data, calculating a correction factor based on the comparison, and applying the correction factor to the client-assigned timestamp.
- a method includes receiving, by one or more servers, a data packet that includes data referencing one or more client-side events, data referencing a respective time that a client has assigned to each event, and data referencing a transmission time that the client has assigned to the data packet.
- the method includes assigning, by the one or more servers, a receipt time to the data packet, and comparing, by the one or more servers, the transmission time that the client has assigned to the data packet to the receipt time that the one or more servers have assigned to the data packet, to determine a time correction factor.
- the method also includes applying, by the one or more servers, the time correction factor to the respective time that the client has assigned to each client-side events, to generate a respective corrected time for each event, and logging, by the one or more servers, the one or more client-side events in an event log according to the respective corrected time for each event.
- Client computers may report records of client-side events to a server without synchronizing with the server's clock, or with a reference clock.
- Client computers may communicate with two different servers whose clocks are not synchronized with each other, or whose clocks are associated with different time zones.
- Client-side events from many clients may be aggregated by a server and stored in an accurate, chronological order.
- FIGS. 1 and 2 are diagrams of example systems that generate and correct client assigned timestamps.
- FIG. 3 is a flow chart illustrating an example process for adjusting a client assigned timestamp to match a server's clock.
- FIG. 4 is a schematic diagram that shows an example of a computing system that may be used in connection with computer-implemented methods and systems described in this document.
- FIG. 1 is a diagram of an example system 100 that generates and corrects client-assigned timestamps.
- FIG. 1 also illustrates an example flow of data, shown in states (A) to (F). States (A) to (F) may occur in the illustrated sequence, or may occur in a sequence that is different than the illustrated sequence.
- a client computer 102 may send a log of client-side events to a server computer 104 over one or more networks 106 .
- the client-side events may be timestamped according to the client computer's 102 clock 110 .
- the clock 110 may not be synchronized with the server computer's 104 clock 116 .
- the server computer 104 may find a time offset between the client timestamp and the clock 116 and apply that offset to the client-side events in the received log.
- the client computer 102 may be a general purpose computer (e.g. a desktop, laptop, tablet computer, or mobile device with an operating system and user installed applications), a dedicated computing device (e.g. a web appliance, feature phone, smart power meter, e-book reader, or media player), another type of computing system, or a collection of any such devices.
- a general purpose computer e.g. a desktop, laptop, tablet computer, or mobile device with an operating system and user installed applications
- a dedicated computing device e.g. a web appliance, feature phone, smart power meter, e-book reader, or media player
- another type of computing system e.g. a collection of any such devices.
- the client computer 102 may include a processor 108 , the clock 110 , and an event log datastore 112 .
- the processor 108 may execute code (e.g. firmware, operating system, and/or applications) that, among other features, generates records of client-side events.
- Client-side events include user inputs (e.g. clicks in a web browser), automated actions (e.g. data capture from sensors attached to the client computer 102 ), and internal client-side events in the client computer 102 (e.g. an anti-virus application scanning a malicious file).
- the clock 110 may keep track of time in the client computer's 102 computing environment and may be used for timing tasks (e.g. scheduled execution of an application, monitoring a time window, timestamping a log entry).
- the clock 110 may track time in an absolute time format. One scheme for tracking absolute time is counting the number of seconds that have passed since a benchmark time.
- the event log datastore 112 may maintain a record of client-side events and associated timestamps from the clock 110 captured when the client-side event was created.
- the server computer 104 may be general purpose computer, a dedicated computing device, a network device (e.g. a network switch, edge router, or telecommunication base-station), a peer of the client computer 102 (e.g. a node in a multi-computer cluster), another type of computing system, or a collection of any such devices.
- the server computer 104 may include a processor 114 , a clock 116 , an event log datastore 118 , and a timestamp correction module 120 .
- the processor 114 may execute code that, among other features, stores client-side events from the client computer 102 .
- the clock 116 may keep track of time in the server computer's 104 computing environment and may be used for timing tasks.
- the clock 116 may track time in an absolute format.
- the event log datastore 118 may maintain a record of received client-side events and associated timestamps, with the timestamps accurate according to the clock 116 .
- the timestamp correction module may examine incoming message packets containing client-side event logs and correct any contained timestamps to corm with the clock 116 .
- the event log datastore 112 of the client computer 102 may maintain some or all client-side events in an event log 122 .
- an event log 122 may be maintained by the event log datastore 112 for each application of the client computer 102 , for each user of the client computer 102 , or according to other criteria.
- New client-side events may appended to the end of the event log, which may effectively ensure that the log is in chronological order, though other schemes are possible.
- Each client-side event in the event log may have an event data-field, a time data-field, and other data-fields.
- the event data-field may, for example, uniquely identify each client-side event in the event log 122 .
- the time data-field may record the state (e.g. time) of the clock 110 when the associated client-side event was created, monitored, or recorded, etc.
- Other data-fields not shown may include an anonymized user identifier, application identifier, event results (e.g. a search result that a user selects), and/or application specific data (e.g. a search session identifier used by a search engine).
- the client computer 102 may transmit some or all of the event log 122 in one or more data packets 124 to the server computer 104 .
- the data packet 124 may include a client-side transmission timestamp indicating the state of the clock 110 when the event log 122 is transmitted from the client computer 102 .
- the client computer 102 may transmit each entry in the event log 122 as it is created.
- the client computer 102 may store a group of client-side event records and transmit them in a bulk event log 122 .
- a search engine interface may store client-side events related to user interactions with the search engine (e.g. queries submitted, search results displayed, search results selected by a user, refinements to submitted queries).
- client-side events e.g. queries submitted, search results displayed, search results selected by a user, refinements to submitted queries.
- the record of these client-side events may include information such as a search session identifier, anonymized user identifier, and click-selections of search results.
- the event log 122 may be transmitted by the client computer 102 at clock 110 time T 20 .
- This transmission may include a ping request, which may be used to estimate latency between the client computer 102 and the server computer 104 .
- the server computer 104 may receive the data packets 124 , and thus the enclosed event log 122 and client-side transmission timestamp.
- the reception by the server computer 104 may include determining, at the time of receipt, a receipt timestamp according to the server computer's 104 clock 116 .
- a discrepancy between the transmission timestamp and receipt timestamp is possible.
- One reason for such a discrepancy may be a mismatch between the clock 110 and the clock 116 . That is, at one particular point in time, the clock 110 and the clock 116 may indicate different times.
- synchronization of clocks may be difficult (e.g. over large geographic distances or for a moving client) or impossible (e.g. if a client must respect two different server clocks, when clock synchronization is not supported by the client or servers).
- Another source of the discrepancy may be network latency. That is, the travel time of the data packet 124 may be large compared to the granularity of the clock 110 and the clock 116 , or the travel time over the link used by the data packet 124 may be highly variable or ‘bursty.’
- the timestamp correction module 120 may compare the receipt timestamp (e.g. server-time T 17 ) with the transmission timestamp (e.g. client-time T 20 ) to determine a time correction factor.
- the time correction factor may be the difference between the receipt timestamp and the transmission timestamp (e.g. 17 ⁇ 20, ⁇ 3). In some implementations, other calculations may be used.
- the timestamp correction module 120 may, in addition to finding the difference in timestamps, account for actual or estimated delays (e.g. network latency).
- the timestamp correction module 120 may add a latency factor to the transmission timestamp or subtract the latency factor from the receipt timestamp. For example, for a transmission timestamp of T 125 , a latency factor of 5, and a receipt timestamp of T 130 , the timestamp correction module 120 may determine a time correction factor of 0 (125+5 ⁇ 130).
- the timestamp correction module 120 may correct the received client-side event timestamps to create a corrected event log 126 .
- the timestamp correction module 120 may edit each record in the event log 122 to add a server-time data-field.
- the server-time data-field may be populated using the client-time data-field and the time correction factor from data state (D).
- the corrected event log 126 may have a client-time data-field value of T 3
- the timestamp correction module 120 may have a time correction factor of ⁇ 3. By adding ⁇ 3 to the time T 3 , a server-time value of T 0 may be found.
- the timestamp correction module 120 may add the received client-side events of the corrected event log 126 to a server event log 128 .
- the server event log may, for example, record some or all client-side events from some or all clients in communication with the server computer 104 .
- the server event log 128 may be sorted or ordered according to the server time of each entry. In such an ordering, the client-side events of the client computer 102 may be separated by client-side events of other clients.
- FIG. 2 is a diagram of an example system 200 that generates and corrects client assigned timestamps.
- the system 200 may include the client computer 102 , the server computer 104 , and the network 106 previously described in FIG. 1 . Elements of the client computer 102 and of the server computer 104 described here may be executed and/or supported by some or all of the elements previously described.
- the client computer 102 may include an event detector 202 , an event logger 204 , the clock 110 , an event queue 208 , a queue manager 210 , and an interface 212 .
- the server computer 104 may include an interface 214 , a latency detector 216 , the clock 116 , a packet manager 218 , a parser 222 , a time correction manager 224 , an event logger 226 , and an event log 228 .
- the event detector 202 may detect client-side events that are internal or external to the client computer. For example, a user clicking in a web browser displaying search results may be one such event that is detected.
- the event detector 202 may pass data of and/or a reference to the client-side event to the event logger 204 .
- the event logger 204 may generate one or more records of the client-side event.
- the event logger may create structured text (e.g. XML) or data (e.g. database rows) capturing information of the user click event.
- the event logger 204 may include a timestamp based on time data provided by the clock 110 .
- the event queue 208 may receive the records of the client-side events from the event logger 204 and may store those records, temporarily or long-term.
- the queue manager 210 may request the records of the client-side events from the event queue 208 .
- the event queue 208 may operate in a first-in, first-out configuration, wherein the oldest records available are served to the queue manager 210 first.
- the queue manager may aggregate records of client-side events for transmission to the server computer 104 .
- the queue manager 210 may construct the aggregation according to one or more criteria, such as a limited time window, an application session, a maximum number of records, etc. Included in this aggregation may be a transmission timestamp indicating the time, according to the clock 110 of the client computer 102 , that the aggregation is transmitted.
- the interface 212 of the client computer 102 may transmit the aggregation in one or more data packets over the networks 106 to the interface 214 of the server computer 104 .
- This transmission may be formatted according to one or more communication standards such as the secure hypertext markup language (HTTPS), the file transfer protocol (FTP), and/or an application program interface (API) such as a representation state transfer (RESTful) API, etc.
- HTTPS secure hypertext markup language
- FTP file transfer protocol
- API application program interface
- RESTful representation state transfer
- the latency detector may detect and/or determine the latency of the communication between the interface 212 and the interface 214 . To determine this latency, the latency detector may use information about ping requests from the client computer, a stored latency value associated with the client computer address or the data packet's anonymized user identifier, or other appropriate measures.
- the packet manager 218 may receive the data packet, latency information, and server time from the interface 214 , the latency detector 216 , and the clock 116 , respectively. A receipt timestamp may be added to the data packet by the packet manager 218 , and the data packet may be passed to the parser 222 .
- the parser 222 may parse the data packet into records of the client-side events. For example, unneeded network information may be identified and stripped, the records may be converted into another format, text may be parsed into n-grams, etc.
- the time correction manager 224 may receive the parsed records and determine a time correction factor for the received data packet. This correction factor may be applied to each record of the received data packet to create time information that is accurate for the server computer 104 .
- the event logger 226 may receive the records with the server computer 104 corrected time information, and store the records in the event log 228 . Once stored, the records may be made available to other elements of the server computer 104 or other computer systems for use in data analysis tasks, billing and record keeping, etc.
- FIG. 3 is a flow chart illustrating an example process 300 for adjusting a client assigned timestamp to match a server's clock.
- the process 300 includes receiving time information and adjusting the time information to a different reference.
- the process 300 may be performed by, for example, the server computer 104 of the systems 100 and/or 200 , but another system, or combination of systems, may also perform the process 300 . While a particular number, type, and order of actions are described here, other numbers, orders, and types of actions may be possible within the process 300 .
- data referencing one or more events may take the form of text, metadata, and other data that describes part of the state of an application or computer system when a predefined condition of the application or computer system is met.
- the data referencing a respective time for each event may take the form of a date and time, a count of time since a landmark, or other appropriate format.
- Data referencing a transmission time may take the form of similarly formatted data.
- the only data received from the client may a reference (e.g. pointer, universal resource locater (URL), etc) to a datastore location that contains this information.
- a reference e.g. pointer, universal resource locater (URL), etc
- URL universal resource locater
- a server-assigned time of packet receipt is determined ( 304 ). For example, a clock associated with the receiving server may be polled when packet receipt occurs.
- the clock may be external to the server and coupled communicably to the server. This may be the case, for example, if a clock service is provided to a collection of servers, and all the servers are configured to work with the same time reference for purposes of interoperability and data exchange.
- a correction factor is determined by comparing client-assigned transmission time to server-assigned receipt time ( 306 ). For example, the difference between the client-assigned transmission time and the server-assigned receipt time may be found and used as the basis for a time correction factor. This difference may be modified to account for factors other than just asynchronism of the client and server system. For example, network lag or processing lag (delay due to generating or parsing, for example) may also be accounted for.
- a latency factor associated with communicating the data packet is determined and applying the time correction factor to generate a respective corrected time for each event includes applying the latency factor.
- (CLIENTPINGTIME ⁇ SERVERPINGTIME) may modified to be ((CLIENTPINGTIME ⁇ SERVERPINGTIME+LAGTIME), where LAGTIME represents some or all estimated lag in communication between the client and server.
- the correction factor is applied to client-assigned timestamps to generate a corrected time for each event ( 308 ). For example, for every timestamp in the receive data packet, except for the transmission timestamp, the correction factor may be applied to translate the timestamp from the reference of the client to the reference of the server. Events are logged according to the corrected timestamps ( 310 ). For example, the events may be merged into an existing event log on the server or accessible by the server. These events may be used for any number of future tasks by the server or another computer device. In some implementations, future incoming events may be processed by the process 300 and stored to the same repository, subject to privacy safeguards and limitations.
- FIG. 4 is a schematic diagram that shows an example of a computing system 400 .
- the computing system 400 may be used for some or all of the operations described previously, according to some implementations.
- the computing system 400 includes a processor 410 , a memory 420 , a storage device 430 , and an input/output device 440 .
- Each of the processor 410 , the memory 420 , the storage device 430 , and the input/output device 440 are interconnected using a system bus 450 .
- the processor 410 is capable of processing instructions for execution within the computing system 400 .
- the processor 410 is a single-threaded processor.
- the processor 410 is a multi-threaded processor.
- the processor 410 is capable of processing instructions stored in the memory 420 or on the storage device 430 to display graphical information for a user interface on the input/output device 440 .
- the memory 420 stores information within the computing system 400 .
- the memory 420 is a computer-readable medium.
- the memory 420 is a volatile memory unit.
- the memory 420 is a non-volatile memory unit.
- the storage device 430 is capable of providing mass storage for the computing system 400 .
- the storage device 430 is a computer-readable medium.
- the storage device 430 may be a floppy disk device, a hard disk device, an optical disk device, or a tape device.
- the input/output device 440 provides input/output operations for the computing system 400 .
- the input/output device 440 includes a keyboard and/or pointing device.
- the input/output device 440 includes a display unit for displaying graphical user interfaces.
- the apparatus may be implemented in a computer program product tangibly embodied in an information carrier, e.g., in a machine-readable storage device, for execution by a programmable processor; and method steps may be performed by a programmable processor executing a program of instructions to perform functions of the described implementations by operating on input data and generating output.
- the described features may be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device.
- a computer program is a set of instructions that may be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result.
- a computer program may be written in any form of programming language, including compiled or interpreted languages, and it may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
- Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors, and the sole processor or one of multiple processors of any kind of computer.
- a processor will receive instructions and data from a read-only memory or a random access memory or both.
- the essential elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data.
- a computer will also include, or be operatively coupled to communicate with, one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks.
- Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM (compact disc read-only memory) and DVD-ROM (digital versatile disc read-only memory) disks.
- semiconductor memory devices such as EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), and flash memory devices
- magnetic disks such as internal hard disks and removable disks
- magneto-optical disks magneto-optical disks
- CD-ROM compact disc read-only memory
- DVD-ROM digital versatile disc read-only memory
- a computer having a display device such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user and a keyboard and a pointing device such as a mouse or a trackball by which the user may provide input to the computer.
- a display device such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user and a keyboard and a pointing device such as a mouse or a trackball by which the user may provide input to the computer.
- Some features may be implemented in a computer system that includes a back-end component, such as a data server, or that includes a middleware component, such as an application server or an Internet server, or that includes a front-end component, such as a client computer having a graphical user interface or an Internet browser, or any combination of them.
- the components of the system may be connected by any form or medium of digital data communication such as a communication network. Examples of communication networks include, e.g., a LAN (local area network), a WAN (wide area network), and the computers and networks forming the Internet.
- the computer system may include clients and servers.
- a client and server are generally remote from each other and typically interact through a network, such as the described one.
- the relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
Abstract
Description
Claims (15)
SERVEREVENTTIME=CLIENTEVENTTIME−(CLIENTPINGTIME−SERVERPINGTIME),
SERVEREVENTTIME=CLIENTEVENTTIME−(CLIENTPINGTIME−SERVERPINGTIME),
SERVEREVENTTIME=CLIENTEVENTTIME−(CLIENTPINGTIME−SERVERPINGTIME),
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Cited By (53)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20160098326A1 (en) * | 2013-05-13 | 2016-04-07 | Freescale Semiconductor, Inc. | Method and apparatus for enabling temporal alignment of debug information |
US9338187B1 (en) * | 2013-11-12 | 2016-05-10 | Emc Corporation | Modeling user working time using authentication events within an enterprise network |
US9430501B1 (en) * | 2012-12-31 | 2016-08-30 | Emc Corporation | Time sanitization of network logs from a geographically distributed computer system |
US9503468B1 (en) | 2013-11-12 | 2016-11-22 | EMC IP Holding Company LLC | Detecting suspicious web traffic from an enterprise network |
US9507798B1 (en) * | 2013-12-30 | 2016-11-29 | EMC IP Holding Company LLC | Centralized logging for a data storage system |
US9516039B1 (en) | 2013-11-12 | 2016-12-06 | EMC IP Holding Company LLC | Behavioral detection of suspicious host activities in an enterprise |
US20160359711A1 (en) * | 2015-06-05 | 2016-12-08 | Cisco Technology, Inc. | Late data detection in data center |
US20170083552A1 (en) * | 2015-09-20 | 2017-03-23 | Google Inc. | Systems and methods for correcting timestamps on data received from untrusted devices |
EP3264302A1 (en) * | 2016-06-29 | 2018-01-03 | CRF Box Oy | Method and apparatus for adjusting event timestamp relating to clinical trial |
US9967158B2 (en) | 2015-06-05 | 2018-05-08 | Cisco Technology, Inc. | Interactive hierarchical network chord diagram for application dependency mapping |
US10033766B2 (en) | 2015-06-05 | 2018-07-24 | Cisco Technology, Inc. | Policy-driven compliance |
US10089099B2 (en) | 2015-06-05 | 2018-10-02 | Cisco Technology, Inc. | Automatic software upgrade |
US10116559B2 (en) | 2015-05-27 | 2018-10-30 | Cisco Technology, Inc. | Operations, administration and management (OAM) in overlay data center environments |
US10142353B2 (en) | 2015-06-05 | 2018-11-27 | Cisco Technology, Inc. | System for monitoring and managing datacenters |
US10171357B2 (en) | 2016-05-27 | 2019-01-01 | Cisco Technology, Inc. | Techniques for managing software defined networking controller in-band communications in a data center network |
US10177977B1 (en) | 2013-02-13 | 2019-01-08 | Cisco Technology, Inc. | Deployment and upgrade of network devices in a network environment |
US10250446B2 (en) | 2017-03-27 | 2019-04-02 | Cisco Technology, Inc. | Distributed policy store |
US10289438B2 (en) | 2016-06-16 | 2019-05-14 | Cisco Technology, Inc. | Techniques for coordination of application components deployed on distributed virtual machines |
EP3512132A1 (en) * | 2018-01-12 | 2019-07-17 | Siemens Aktiengesellschaft | Method for the temporal synchronisation of data transmitters with a data receiver in a data network |
US10374904B2 (en) | 2015-05-15 | 2019-08-06 | Cisco Technology, Inc. | Diagnostic network visualization |
US10523512B2 (en) | 2017-03-24 | 2019-12-31 | Cisco Technology, Inc. | Network agent for generating platform specific network policies |
US10523541B2 (en) | 2017-10-25 | 2019-12-31 | Cisco Technology, Inc. | Federated network and application data analytics platform |
US10554501B2 (en) | 2017-10-23 | 2020-02-04 | Cisco Technology, Inc. | Network migration assistant |
US10574575B2 (en) | 2018-01-25 | 2020-02-25 | Cisco Technology, Inc. | Network flow stitching using middle box flow stitching |
US10594560B2 (en) | 2017-03-27 | 2020-03-17 | Cisco Technology, Inc. | Intent driven network policy platform |
US10594542B2 (en) | 2017-10-27 | 2020-03-17 | Cisco Technology, Inc. | System and method for network root cause analysis |
US10680887B2 (en) | 2017-07-21 | 2020-06-09 | Cisco Technology, Inc. | Remote device status audit and recovery |
US10708152B2 (en) | 2017-03-23 | 2020-07-07 | Cisco Technology, Inc. | Predicting application and network performance |
US10708183B2 (en) | 2016-07-21 | 2020-07-07 | Cisco Technology, Inc. | System and method of providing segment routing as a service |
US10764141B2 (en) | 2017-03-27 | 2020-09-01 | Cisco Technology, Inc. | Network agent for reporting to a network policy system |
US10798015B2 (en) | 2018-01-25 | 2020-10-06 | Cisco Technology, Inc. | Discovery of middleboxes using traffic flow stitching |
US10826803B2 (en) | 2018-01-25 | 2020-11-03 | Cisco Technology, Inc. | Mechanism for facilitating efficient policy updates |
JP2020191498A (en) * | 2019-05-20 | 2020-11-26 | 三菱電機株式会社 | Data correction method and data correction device |
US10873593B2 (en) | 2018-01-25 | 2020-12-22 | Cisco Technology, Inc. | Mechanism for identifying differences between network snapshots |
US10873794B2 (en) | 2017-03-28 | 2020-12-22 | Cisco Technology, Inc. | Flowlet resolution for application performance monitoring and management |
US10917438B2 (en) | 2018-01-25 | 2021-02-09 | Cisco Technology, Inc. | Secure publishing for policy updates |
US10931629B2 (en) | 2016-05-27 | 2021-02-23 | Cisco Technology, Inc. | Techniques for managing software defined networking controller in-band communications in a data center network |
US10972388B2 (en) | 2016-11-22 | 2021-04-06 | Cisco Technology, Inc. | Federated microburst detection |
US10999149B2 (en) | 2018-01-25 | 2021-05-04 | Cisco Technology, Inc. | Automatic configuration discovery based on traffic flow data |
US11012955B2 (en) * | 2018-03-20 | 2021-05-18 | International Business Machines Corporation | Synchronization of host and client log timestamps |
US11128700B2 (en) | 2018-01-26 | 2021-09-21 | Cisco Technology, Inc. | Load balancing configuration based on traffic flow telemetry |
US11159550B1 (en) * | 2019-03-01 | 2021-10-26 | Chronicle Llc | Correcting timestamps for computer security telemetry data |
US11233821B2 (en) | 2018-01-04 | 2022-01-25 | Cisco Technology, Inc. | Network intrusion counter-intelligence |
US20220352998A1 (en) * | 2021-05-03 | 2022-11-03 | Mellanox Technologies, Ltd. | Timestamp Confidence Level |
US20220408391A1 (en) * | 2021-06-22 | 2022-12-22 | Getac Technology Corporation | Clock synchronization and data redundancy for a mesh network of user devices |
US11606111B2 (en) | 2021-05-26 | 2023-03-14 | Getac Technology Corporation | Adaptive power and communication routing for body-worn devices |
US11606157B1 (en) | 2021-11-07 | 2023-03-14 | Mellanox Technologies, Ltd. | Time synchronization based on network traffic patterns |
US20230259156A1 (en) * | 2022-02-14 | 2023-08-17 | Click Therapeutics, Inc. | Estimation of Event Generation Times to Synchronize Recordation Data |
WO2023154059A1 (en) * | 2022-02-14 | 2023-08-17 | Click Therapeutics, Inc. | Estimation of event generation times to synchronize recordation data |
US11765046B1 (en) | 2018-01-11 | 2023-09-19 | Cisco Technology, Inc. | Endpoint cluster assignment and query generation |
US11808823B2 (en) | 2021-06-02 | 2023-11-07 | Getac Technology Corporation | Detection of device dislocation using power and non-powered dislocation sensors |
US11864271B2 (en) | 2021-09-21 | 2024-01-02 | Getac Technology Corporation | Mobile device ID tracking for automatic incident data association and correlation |
US11953939B2 (en) * | 2022-03-03 | 2024-04-09 | Oversec, Uab | Clock management systems and methods |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6327274B1 (en) | 1998-09-15 | 2001-12-04 | Nokia Telecommunications, Inc. | Method for estimating relative skew between clocks in packet networks |
US7023884B2 (en) | 2000-12-19 | 2006-04-04 | Lucent Technologies Inc. | Clock offset estimation with bias correction |
US20090210784A1 (en) * | 2008-02-14 | 2009-08-20 | Oracle International Corporation | Active data push delivery |
US7609207B2 (en) | 2006-03-03 | 2009-10-27 | Cisco Technology, Inc. | System and method for performing time difference of arrival location without requiring a common time base or clock calibration |
US20100057939A1 (en) * | 2008-08-28 | 2010-03-04 | Yahoo! Inc. | Delivering Partially Processed Results Based on System Metrics in Network Content Delivery Systems |
US7702909B2 (en) * | 2003-12-22 | 2010-04-20 | Klimenty Vainstein | Method and system for validating timestamps |
US7969891B2 (en) | 2007-04-24 | 2011-06-28 | Microsoft Corporation | Adjustment of clock approximations |
US8059688B2 (en) | 2007-12-31 | 2011-11-15 | Intel Corporation | Synchronizing multiple system clocks |
-
2011
- 2011-07-29 US US13/194,892 patent/US8719452B1/en not_active Expired - Fee Related
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6327274B1 (en) | 1998-09-15 | 2001-12-04 | Nokia Telecommunications, Inc. | Method for estimating relative skew between clocks in packet networks |
US7023884B2 (en) | 2000-12-19 | 2006-04-04 | Lucent Technologies Inc. | Clock offset estimation with bias correction |
US7702909B2 (en) * | 2003-12-22 | 2010-04-20 | Klimenty Vainstein | Method and system for validating timestamps |
US7609207B2 (en) | 2006-03-03 | 2009-10-27 | Cisco Technology, Inc. | System and method for performing time difference of arrival location without requiring a common time base or clock calibration |
US7969891B2 (en) | 2007-04-24 | 2011-06-28 | Microsoft Corporation | Adjustment of clock approximations |
US8059688B2 (en) | 2007-12-31 | 2011-11-15 | Intel Corporation | Synchronizing multiple system clocks |
US20090210784A1 (en) * | 2008-02-14 | 2009-08-20 | Oracle International Corporation | Active data push delivery |
US20100057939A1 (en) * | 2008-08-28 | 2010-03-04 | Yahoo! Inc. | Delivering Partially Processed Results Based on System Metrics in Network Content Delivery Systems |
Cited By (138)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9430501B1 (en) * | 2012-12-31 | 2016-08-30 | Emc Corporation | Time sanitization of network logs from a geographically distributed computer system |
US10177977B1 (en) | 2013-02-13 | 2019-01-08 | Cisco Technology, Inc. | Deployment and upgrade of network devices in a network environment |
US20160098326A1 (en) * | 2013-05-13 | 2016-04-07 | Freescale Semiconductor, Inc. | Method and apparatus for enabling temporal alignment of debug information |
US10169171B2 (en) * | 2013-05-13 | 2019-01-01 | Nxp Usa, Inc. | Method and apparatus for enabling temporal alignment of debug information |
US9338187B1 (en) * | 2013-11-12 | 2016-05-10 | Emc Corporation | Modeling user working time using authentication events within an enterprise network |
US9503468B1 (en) | 2013-11-12 | 2016-11-22 | EMC IP Holding Company LLC | Detecting suspicious web traffic from an enterprise network |
US9516039B1 (en) | 2013-11-12 | 2016-12-06 | EMC IP Holding Company LLC | Behavioral detection of suspicious host activities in an enterprise |
US9507798B1 (en) * | 2013-12-30 | 2016-11-29 | EMC IP Holding Company LLC | Centralized logging for a data storage system |
US10374904B2 (en) | 2015-05-15 | 2019-08-06 | Cisco Technology, Inc. | Diagnostic network visualization |
US10116559B2 (en) | 2015-05-27 | 2018-10-30 | Cisco Technology, Inc. | Operations, administration and management (OAM) in overlay data center environments |
US11121948B2 (en) | 2015-06-05 | 2021-09-14 | Cisco Technology, Inc. | Auto update of sensor configuration |
US10567247B2 (en) | 2015-06-05 | 2020-02-18 | Cisco Technology, Inc. | Intra-datacenter attack detection |
US10009240B2 (en) | 2015-06-05 | 2018-06-26 | Cisco Technology, Inc. | System and method of recommending policies that result in particular reputation scores for hosts |
US10033766B2 (en) | 2015-06-05 | 2018-07-24 | Cisco Technology, Inc. | Policy-driven compliance |
US10089099B2 (en) | 2015-06-05 | 2018-10-02 | Cisco Technology, Inc. | Automatic software upgrade |
US10116531B2 (en) | 2015-06-05 | 2018-10-30 | Cisco Technology, Inc | Round trip time (RTT) measurement based upon sequence number |
US11128552B2 (en) | 2015-06-05 | 2021-09-21 | Cisco Technology, Inc. | Round trip time (RTT) measurement based upon sequence number |
US10735283B2 (en) | 2015-06-05 | 2020-08-04 | Cisco Technology, Inc. | Unique ID generation for sensors |
US10129117B2 (en) | 2015-06-05 | 2018-11-13 | Cisco Technology, Inc. | Conditional policies |
US10142353B2 (en) | 2015-06-05 | 2018-11-27 | Cisco Technology, Inc. | System for monitoring and managing datacenters |
US10171319B2 (en) | 2015-06-05 | 2019-01-01 | Cisco Technology, Inc. | Technologies for annotating process and user information for network flows |
US11637762B2 (en) | 2015-06-05 | 2023-04-25 | Cisco Technology, Inc. | MDL-based clustering for dependency mapping |
US9967158B2 (en) | 2015-06-05 | 2018-05-08 | Cisco Technology, Inc. | Interactive hierarchical network chord diagram for application dependency mapping |
US11695659B2 (en) | 2015-06-05 | 2023-07-04 | Cisco Technology, Inc. | Unique ID generation for sensors |
US10177998B2 (en) | 2015-06-05 | 2019-01-08 | Cisco Technology, Inc. | Augmenting flow data for improved network monitoring and management |
US10181987B2 (en) | 2015-06-05 | 2019-01-15 | Cisco Technology, Inc. | High availability of collectors of traffic reported by network sensors |
US10230597B2 (en) | 2015-06-05 | 2019-03-12 | Cisco Technology, Inc. | Optimizations for application dependency mapping |
US10243817B2 (en) | 2015-06-05 | 2019-03-26 | Cisco Technology, Inc. | System and method of assigning reputation scores to hosts |
US11601349B2 (en) | 2015-06-05 | 2023-03-07 | Cisco Technology, Inc. | System and method of detecting hidden processes by analyzing packet flows |
US11700190B2 (en) | 2015-06-05 | 2023-07-11 | Cisco Technology, Inc. | Technologies for annotating process and user information for network flows |
US10305757B2 (en) | 2015-06-05 | 2019-05-28 | Cisco Technology, Inc. | Determining a reputation of a network entity |
US10320630B2 (en) | 2015-06-05 | 2019-06-11 | Cisco Technology, Inc. | Hierarchichal sharding of flows from sensors to collectors |
US10326672B2 (en) | 2015-06-05 | 2019-06-18 | Cisco Technology, Inc. | MDL-based clustering for application dependency mapping |
US10326673B2 (en) | 2015-06-05 | 2019-06-18 | Cisco Technology, Inc. | Techniques for determining network topologies |
US11153184B2 (en) | 2015-06-05 | 2021-10-19 | Cisco Technology, Inc. | Technologies for annotating process and user information for network flows |
US11968103B2 (en) | 2015-06-05 | 2024-04-23 | Cisco Technology, Inc. | Policy utilization analysis |
US20160359711A1 (en) * | 2015-06-05 | 2016-12-08 | Cisco Technology, Inc. | Late data detection in data center |
US10439904B2 (en) | 2015-06-05 | 2019-10-08 | Cisco Technology, Inc. | System and method of determining malicious processes |
US10454793B2 (en) | 2015-06-05 | 2019-10-22 | Cisco Technology, Inc. | System and method of detecting whether a source of a packet flow transmits packets which bypass an operating system stack |
US10505828B2 (en) | 2015-06-05 | 2019-12-10 | Cisco Technology, Inc. | Technologies for managing compromised sensors in virtualized environments |
US10505827B2 (en) | 2015-06-05 | 2019-12-10 | Cisco Technology, Inc. | Creating classifiers for servers and clients in a network |
US10516586B2 (en) | 2015-06-05 | 2019-12-24 | Cisco Technology, Inc. | Identifying bogon address spaces |
US10516585B2 (en) | 2015-06-05 | 2019-12-24 | Cisco Technology, Inc. | System and method for network information mapping and displaying |
US11528283B2 (en) | 2015-06-05 | 2022-12-13 | Cisco Technology, Inc. | System for monitoring and managing datacenters |
US11522775B2 (en) | 2015-06-05 | 2022-12-06 | Cisco Technology, Inc. | Application monitoring prioritization |
US10536357B2 (en) * | 2015-06-05 | 2020-01-14 | Cisco Technology, Inc. | Late data detection in data center |
US11516098B2 (en) | 2015-06-05 | 2022-11-29 | Cisco Technology, Inc. | Round trip time (RTT) measurement based upon sequence number |
US11102093B2 (en) | 2015-06-05 | 2021-08-24 | Cisco Technology, Inc. | System and method of assigning reputation scores to hosts |
US11968102B2 (en) | 2015-06-05 | 2024-04-23 | Cisco Technology, Inc. | System and method of detecting packet loss in a distributed sensor-collector architecture |
US11502922B2 (en) | 2015-06-05 | 2022-11-15 | Cisco Technology, Inc. | Technologies for managing compromised sensors in virtualized environments |
US11496377B2 (en) | 2015-06-05 | 2022-11-08 | Cisco Technology, Inc. | Anomaly detection through header field entropy |
US10623283B2 (en) | 2015-06-05 | 2020-04-14 | Cisco Technology, Inc. | Anomaly detection through header field entropy |
US10623282B2 (en) | 2015-06-05 | 2020-04-14 | Cisco Technology, Inc. | System and method of detecting hidden processes by analyzing packet flows |
US10623284B2 (en) | 2015-06-05 | 2020-04-14 | Cisco Technology, Inc. | Determining a reputation of a network entity |
US10659324B2 (en) | 2015-06-05 | 2020-05-19 | Cisco Technology, Inc. | Application monitoring prioritization |
US11477097B2 (en) | 2015-06-05 | 2022-10-18 | Cisco Technology, Inc. | Hierarchichal sharding of flows from sensors to collectors |
US10686804B2 (en) | 2015-06-05 | 2020-06-16 | Cisco Technology, Inc. | System for monitoring and managing datacenters |
US10693749B2 (en) | 2015-06-05 | 2020-06-23 | Cisco Technology, Inc. | Synthetic data for determining health of a network security system |
US10742529B2 (en) | 2015-06-05 | 2020-08-11 | Cisco Technology, Inc. | Hierarchichal sharding of flows from sensors to collectors |
US11431592B2 (en) | 2015-06-05 | 2022-08-30 | Cisco Technology, Inc. | System and method of detecting whether a source of a packet flow transmits packets which bypass an operating system stack |
US9979615B2 (en) | 2015-06-05 | 2018-05-22 | Cisco Technology, Inc. | Techniques for determining network topologies |
US10116530B2 (en) | 2015-06-05 | 2018-10-30 | Cisco Technology, Inc. | Technologies for determining sensor deployment characteristics |
US11405291B2 (en) | 2015-06-05 | 2022-08-02 | Cisco Technology, Inc. | Generate a communication graph using an application dependency mapping (ADM) pipeline |
US11368378B2 (en) | 2015-06-05 | 2022-06-21 | Cisco Technology, Inc. | Identifying bogon address spaces |
US10797970B2 (en) | 2015-06-05 | 2020-10-06 | Cisco Technology, Inc. | Interactive hierarchical network chord diagram for application dependency mapping |
US11936663B2 (en) | 2015-06-05 | 2024-03-19 | Cisco Technology, Inc. | System for monitoring and managing datacenters |
US10797973B2 (en) | 2015-06-05 | 2020-10-06 | Cisco Technology, Inc. | Server-client determination |
US11924072B2 (en) | 2015-06-05 | 2024-03-05 | Cisco Technology, Inc. | Technologies for annotating process and user information for network flows |
US11924073B2 (en) | 2015-06-05 | 2024-03-05 | Cisco Technology, Inc. | System and method of assigning reputation scores to hosts |
US10862776B2 (en) | 2015-06-05 | 2020-12-08 | Cisco Technology, Inc. | System and method of spoof detection |
US11902120B2 (en) | 2015-06-05 | 2024-02-13 | Cisco Technology, Inc. | Synthetic data for determining health of a network security system |
US20220131773A1 (en) * | 2015-06-05 | 2022-04-28 | Cisco Technology, Inc. | Data center traffic analytics synchronization |
US11252060B2 (en) * | 2015-06-05 | 2022-02-15 | Cisco Technology, Inc. | Data center traffic analytics synchronization |
US10904116B2 (en) | 2015-06-05 | 2021-01-26 | Cisco Technology, Inc. | Policy utilization analysis |
US10917319B2 (en) | 2015-06-05 | 2021-02-09 | Cisco Technology, Inc. | MDL-based clustering for dependency mapping |
US11902121B2 (en) | 2015-06-05 | 2024-02-13 | Cisco Technology, Inc. | System and method of detecting whether a source of a packet flow transmits packets which bypass an operating system stack |
US10728119B2 (en) | 2015-06-05 | 2020-07-28 | Cisco Technology, Inc. | Cluster discovery via multi-domain fusion for application dependency mapping |
US11252058B2 (en) | 2015-06-05 | 2022-02-15 | Cisco Technology, Inc. | System and method for user optimized application dependency mapping |
US10979322B2 (en) | 2015-06-05 | 2021-04-13 | Cisco Technology, Inc. | Techniques for determining network anomalies in data center networks |
US11902122B2 (en) | 2015-06-05 | 2024-02-13 | Cisco Technology, Inc. | Application monitoring prioritization |
US11894996B2 (en) | 2015-06-05 | 2024-02-06 | Cisco Technology, Inc. | Technologies for annotating process and user information for network flows |
US20170083552A1 (en) * | 2015-09-20 | 2017-03-23 | Google Inc. | Systems and methods for correcting timestamps on data received from untrusted devices |
US9977724B2 (en) * | 2015-09-20 | 2018-05-22 | Google Llc | Systems and methods for correcting timestamps on data received from untrusted devices |
US10931629B2 (en) | 2016-05-27 | 2021-02-23 | Cisco Technology, Inc. | Techniques for managing software defined networking controller in-band communications in a data center network |
US10171357B2 (en) | 2016-05-27 | 2019-01-01 | Cisco Technology, Inc. | Techniques for managing software defined networking controller in-band communications in a data center network |
US11546288B2 (en) | 2016-05-27 | 2023-01-03 | Cisco Technology, Inc. | Techniques for managing software defined networking controller in-band communications in a data center network |
US10289438B2 (en) | 2016-06-16 | 2019-05-14 | Cisco Technology, Inc. | Techniques for coordination of application components deployed on distributed virtual machines |
US11657904B2 (en) | 2016-06-29 | 2023-05-23 | Crf Box Oy | Method and apparatus for adjusting event timestamp relating to clinical trial |
EP3264302A1 (en) * | 2016-06-29 | 2018-01-03 | CRF Box Oy | Method and apparatus for adjusting event timestamp relating to clinical trial |
US11283712B2 (en) | 2016-07-21 | 2022-03-22 | Cisco Technology, Inc. | System and method of providing segment routing as a service |
US10708183B2 (en) | 2016-07-21 | 2020-07-07 | Cisco Technology, Inc. | System and method of providing segment routing as a service |
US10972388B2 (en) | 2016-11-22 | 2021-04-06 | Cisco Technology, Inc. | Federated microburst detection |
US11088929B2 (en) | 2017-03-23 | 2021-08-10 | Cisco Technology, Inc. | Predicting application and network performance |
US10708152B2 (en) | 2017-03-23 | 2020-07-07 | Cisco Technology, Inc. | Predicting application and network performance |
US11252038B2 (en) | 2017-03-24 | 2022-02-15 | Cisco Technology, Inc. | Network agent for generating platform specific network policies |
US10523512B2 (en) | 2017-03-24 | 2019-12-31 | Cisco Technology, Inc. | Network agent for generating platform specific network policies |
US10764141B2 (en) | 2017-03-27 | 2020-09-01 | Cisco Technology, Inc. | Network agent for reporting to a network policy system |
US10250446B2 (en) | 2017-03-27 | 2019-04-02 | Cisco Technology, Inc. | Distributed policy store |
US11146454B2 (en) | 2017-03-27 | 2021-10-12 | Cisco Technology, Inc. | Intent driven network policy platform |
US10594560B2 (en) | 2017-03-27 | 2020-03-17 | Cisco Technology, Inc. | Intent driven network policy platform |
US11509535B2 (en) | 2017-03-27 | 2022-11-22 | Cisco Technology, Inc. | Network agent for reporting to a network policy system |
US10873794B2 (en) | 2017-03-28 | 2020-12-22 | Cisco Technology, Inc. | Flowlet resolution for application performance monitoring and management |
US11202132B2 (en) | 2017-03-28 | 2021-12-14 | Cisco Technology, Inc. | Application performance monitoring and management platform with anomalous flowlet resolution |
US11683618B2 (en) | 2017-03-28 | 2023-06-20 | Cisco Technology, Inc. | Application performance monitoring and management platform with anomalous flowlet resolution |
US11863921B2 (en) | 2017-03-28 | 2024-01-02 | Cisco Technology, Inc. | Application performance monitoring and management platform with anomalous flowlet resolution |
US10680887B2 (en) | 2017-07-21 | 2020-06-09 | Cisco Technology, Inc. | Remote device status audit and recovery |
US10554501B2 (en) | 2017-10-23 | 2020-02-04 | Cisco Technology, Inc. | Network migration assistant |
US11044170B2 (en) | 2017-10-23 | 2021-06-22 | Cisco Technology, Inc. | Network migration assistant |
US10523541B2 (en) | 2017-10-25 | 2019-12-31 | Cisco Technology, Inc. | Federated network and application data analytics platform |
US10594542B2 (en) | 2017-10-27 | 2020-03-17 | Cisco Technology, Inc. | System and method for network root cause analysis |
US10904071B2 (en) | 2017-10-27 | 2021-01-26 | Cisco Technology, Inc. | System and method for network root cause analysis |
US11750653B2 (en) | 2018-01-04 | 2023-09-05 | Cisco Technology, Inc. | Network intrusion counter-intelligence |
US11233821B2 (en) | 2018-01-04 | 2022-01-25 | Cisco Technology, Inc. | Network intrusion counter-intelligence |
US11765046B1 (en) | 2018-01-11 | 2023-09-19 | Cisco Technology, Inc. | Endpoint cluster assignment and query generation |
EP3512132A1 (en) * | 2018-01-12 | 2019-07-17 | Siemens Aktiengesellschaft | Method for the temporal synchronisation of data transmitters with a data receiver in a data network |
WO2019137771A1 (en) * | 2018-01-12 | 2019-07-18 | Siemens Aktiengesellschaft | Method for chronological synchronisation of data transmitters with a data receiver in a data network |
US11924240B2 (en) | 2018-01-25 | 2024-03-05 | Cisco Technology, Inc. | Mechanism for identifying differences between network snapshots |
US10873593B2 (en) | 2018-01-25 | 2020-12-22 | Cisco Technology, Inc. | Mechanism for identifying differences between network snapshots |
US10917438B2 (en) | 2018-01-25 | 2021-02-09 | Cisco Technology, Inc. | Secure publishing for policy updates |
US10574575B2 (en) | 2018-01-25 | 2020-02-25 | Cisco Technology, Inc. | Network flow stitching using middle box flow stitching |
US10798015B2 (en) | 2018-01-25 | 2020-10-06 | Cisco Technology, Inc. | Discovery of middleboxes using traffic flow stitching |
US10999149B2 (en) | 2018-01-25 | 2021-05-04 | Cisco Technology, Inc. | Automatic configuration discovery based on traffic flow data |
US10826803B2 (en) | 2018-01-25 | 2020-11-03 | Cisco Technology, Inc. | Mechanism for facilitating efficient policy updates |
US11128700B2 (en) | 2018-01-26 | 2021-09-21 | Cisco Technology, Inc. | Load balancing configuration based on traffic flow telemetry |
US11012955B2 (en) * | 2018-03-20 | 2021-05-18 | International Business Machines Corporation | Synchronization of host and client log timestamps |
US11159550B1 (en) * | 2019-03-01 | 2021-10-26 | Chronicle Llc | Correcting timestamps for computer security telemetry data |
JP2020191498A (en) * | 2019-05-20 | 2020-11-26 | 三菱電機株式会社 | Data correction method and data correction device |
US11641245B2 (en) * | 2021-05-03 | 2023-05-02 | Mellanox Technologies, Ltd. | Timestamp confidence level |
US20220352998A1 (en) * | 2021-05-03 | 2022-11-03 | Mellanox Technologies, Ltd. | Timestamp Confidence Level |
US11606111B2 (en) | 2021-05-26 | 2023-03-14 | Getac Technology Corporation | Adaptive power and communication routing for body-worn devices |
US11808823B2 (en) | 2021-06-02 | 2023-11-07 | Getac Technology Corporation | Detection of device dislocation using power and non-powered dislocation sensors |
US20220408391A1 (en) * | 2021-06-22 | 2022-12-22 | Getac Technology Corporation | Clock synchronization and data redundancy for a mesh network of user devices |
US11696247B2 (en) * | 2021-06-22 | 2023-07-04 | Getac Technology Corporation | Clock synchronization and data redundancy for a mesh network of user devices |
US11864271B2 (en) | 2021-09-21 | 2024-01-02 | Getac Technology Corporation | Mobile device ID tracking for automatic incident data association and correlation |
US11606157B1 (en) | 2021-11-07 | 2023-03-14 | Mellanox Technologies, Ltd. | Time synchronization based on network traffic patterns |
WO2023154059A1 (en) * | 2022-02-14 | 2023-08-17 | Click Therapeutics, Inc. | Estimation of event generation times to synchronize recordation data |
US20230259156A1 (en) * | 2022-02-14 | 2023-08-17 | Click Therapeutics, Inc. | Estimation of Event Generation Times to Synchronize Recordation Data |
US11953939B2 (en) * | 2022-03-03 | 2024-04-09 | Oversec, Uab | Clock management systems and methods |
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